Methods and systems disclosed herein relate generally to image enhancement and in particular to the enhancement of atmospheric data within images. As the earth science community matures in its pursuit of interdisciplinary research, there is a growing appreciation for the importance of mineral dust and other atmospheric and meteorological parameters such as, but not limited to, fog and volcanic ash, to a myriad components of the earth/atmosphere system. For example, lofted dust can impact i) the radiation balance in the atmosphere via both direct reflective/insulation processes and indirect cloud-altering processes, ii) surface hydrology via snow/ice albedo depression and increased melting rates, and iii) the oceanic ecosystem via deposition of iron-rich nutrients that stimulate phytoplankton growth. To everyday life, atmospheric dust plays a significant role in defining air quality, contributing to human respiratory health problems and degraded atmospheric visibility, a particular concern for aviation. In a changing climate system, traditional source regions for atmospheric dust may evolve with the advance of deserts in some areas and perhaps the vegetative binding of erodible surfaces in other areas. Given the integrated role of dust and other atmospheric parameters in earth system processes, the potential climate response must be considered holistically, via fully coupled earth system models that include these parameters as active, prognostic variables. Accomplishing this physical coupling is a grand challenge and exciting frontier of modern science.
Critical to the understanding of atmospheric and meteorological processes at local, regional, and teleconnected global scales is an observing system capable of resolving the spatio/temporal variability of these processes globally. These data can be collected by a satellite platform, the constellation of which includes optical-spectrum radiometers capable of detecting and quantifying the properties of atmospheric parameters, for example, but not limited to, dust. A sensor that can offer a diversity of spectral information operates on low-earth-orbiting satellites (LEO), providing an improvement over geostationary (GEO) satellites. However, the GEO platform continues to improve as next-generation radiometers join its ranks. The first among them, the EUMETSAT Meteosat Second Generation (MSG), has introduced to GEO new spectral bands that are very useful in dust detection.
Current methods of detecting dust include NASA's Total Ozone Mapping Spectrometer (TOMS) aerosol index, blue light normalized difference dust index (NDDI), thermal infrared split window tri-spectra that enhances the emissivity of dust at different wavelengths, optical remote sensing technology Light Detection And Ranging (LIDAR) which provides a profile of the atmosphere and can detect optically thin parameters like dust. All current techniques have limitations and uncertainties, including, but not limited to, high false alarm rates
Current atmospheric correction systems include models that use a background (averaged) image as their reference point and attempt to get rid of the atmospheric signal so that a better view of the surface can be achieved. However, what is needed for parameter enhancement is minimization of the contribution from the surface. In the prior art, a reference background is used to determine (via a model) a correction parameter which is applied to correct for atmospheric contamination at each location. An implicit assumption is that the aerosol properties of the atmosphere never change (purely molecular scatter), and that the change in scene brightness is therefore purely a function of the height of the observer. At least one drawback is that the prior art requires knowledge of distance from the viewer to each location of terrain. Further, the prior art does not account for atmospheric scatter which is a function of the sun and viewer geometry.
What is needed to work with new satellite platforms is a new, multi-spectral satellite algorithm that utilizes ancillary surface data to overcome traditional challenges to the detection of atmospheric parameters, for example, but not limited to, dust over barren land surfaces. Specifically, what is needed is an approach that incorporates a surface emissivity database, derived for different surface types, as a means for suppressing the erroneous enhancement of land surface features while retaining the ability to detect the atmospheric parameter above these surfaces. The approach should be applicable to both day and nighttime conditions, over land and water, and should use of an optimal combination of spectral information for each of day and night conditions, and a blend of the two across the terminator for near-seamless transition. The approach should provide a quantitative measurement as a confidence factor [0,1] that can be used for visualization as well, presented in the context of the meteorological situation responsible for the atmospheric parameter's presence and movement. The approach should be useful to both automated processes and human users alike. What is further needed is a system that includes multiple parameter detection tests, each having a spatially-resolved background value. What is further needed is the use of the background to determine the strength of contribution from the test that it corresponds to, and combining the results of the individual scores from the tests in a weighted way to form an overall ‘score’, upon which enhanced imagery can be based.